Sensor fault-tolerant observer applied in satellite attitude control

The observing failure and feedback instability might happen when the partial sensors of a satellite attitude control system (SACS) go wrong. A fault diagnosis and isolation (FDI) method based on a fault observer is introduced to detect and isolate the fault sensor at first. Based on the FDI result, the object system state-space equation is transformed and divided into a corresponsive triangular canonical form to decouple the normal subsystem from the fault subsystem. And then the KX fault-tolerant observers of the system in different modes are designed and embedded into online monitoring. The outputs of all KX fault-tolerant observers are selected by the control switch process. That can make sense that the SACS is part-observed and in stable when the partial sensors break down. Simulation results demonstrate the effectiveness and superiority of the proposed method.

System identification, estimation and control for a cost effective open-source quadcopter

This paper describes system identification, estimation and control of translational motion and heading angle for a cost effective open-source quadcopter — the MikroKopter. The dynamics of its built-in sensors, roll and pitch attitude controller, and system latencies are determined and used to design a computationally inexpensive multi-rate velocity estimator that fuses data from the built-in inertial sensors and a low-rate onboard laser range finder. Control is performed using a nested loop structure that is also computationally inexpensive and incorporates different sensors. Experimental results for the estimator and closed-loop positioning are presented and compared with ground truth from a motion capture system.

Sensing and control for a small-size helicopter

This paper details the progress to date, toward developing a small autonomous helicopter. We describe system architecture, avionics, visual state estimation, custom IMU design, aircraft modelling, as well as various linear and neuro/fuzzy control algorithms. Experimental results are presented for state estimation using fused stereo vision and IMU data, heading control, and attitude control. FAM attitude and velocity controllers have been shown to be effective in simulation.

Non-linear model predictive control for UAVs with slung/swung load

Suspended loads on UAVs can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present software and flight system architecture to test controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions.

Multi-rotor with suspended load: System dynamics and control toolbox

There is an increasing demand for Unmanned Aerial Systems (UAS) to carry suspended loads as this can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. The constant variation in operating point induced by the slung load also causes conventional controllers to demand increased control effort. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present a novel controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions. The paper describes a System Dynamics and Control Simulation Toolbox for use with MATLAB/SIMULINK which includes a detailed simulation of the multi-rotor and slung load as well as a predictive controller to manage the nonlinear dynamics whilst accounting for system constraints. It is demonstrated that the controller simultaneously tracks specified waypoints and actively damps large slung load oscillations. A linear-quadratic regulator (LQR) is derived and control performance is compared. Results show the improved performance of the predictive controller for a larger flight envelope, including aggressive manoeuvres and large slung load displacements. The computational cost remains relatively small, amenable to practical implementations.

Social influences and the physical activity intentions of parents of young-children families: An extended Theory of Planned Behavior Approach

Evidence within Australia and internationally suggests parenthood as a risk factor for inactivity; however, research into understanding parental physical activity is scarce. Given that active parents can create active families and social factors are important for parents’ decision making, the authors investigated a range of social influences on parents’ intentions to be physically active. Parents (N = 580; 288 mothers and 292 fathers) of children younger than 5 years completed an extended Theory of Planned Behavior questionnaire either online or paper based. For both genders, attitude, control factors, group norms, friend general support, and an active parent identity predicted intentions, with social pressure and family support further predicting mothers’ intentions and active others further predicting fathers’ intentions. Attention to these factors and those specific to the genders may improve parents’ intentions to be physically active, thus maximizing the benefits to their own health and the healthy lifestyle practices for other family members.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Unmanned Aerial Vehicles UAVs attitude, height, motion estimation and control using visual systems

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV’s attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV’s motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COLIBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

Modelling and control of a large quadrotor robot

Typical quadrotor aerial robots used in research weigh inlMMLBox and carry payloads measured in hundreds of grams. Several obstacles in design and control must be overcome to cater for expected industry demands that push the boundaries of existing quadrotor performance. The X-4 Flyer, a 4 kg quadrotor with a 1 kg payload, is intended to be prototypical of useful commercial quadrotors. The custom-built craft uses tuned plant dynamics with an onboard embedded attitude controller to stabilise flight. Independent linear SISO controllers were designed to regulate flyer attitude. The performance of the system is demonstrated in indoor and outdoor flight.

Sensing and control on the sphere

The advantages of a spherical imaging model are increasingly well recognized within the robotics community. Perhaps less well known is the use of the sphere for attitude estimation, control and scene structure estimation. This paper proposes the sphere as a unifying concept, not just for cameras, but for sensor fusion, estimation and control. We review and summarize relevant work in these areas and illustrate this with relevant simulation examples for spherical visual servoing and scene structure estimation.

Modelling and control of a quad-rotor robot

To date, most quad-rotor aerial robots have been based on flying toys. Although such systems can be used as prototypes, they are not sufficiently robust to serve as experimental robotics platforms. We have developed the X-4 Flyer, a quad-rotor robot using custom-built chassis and avionics with off-the-shelf motors and batteries, to be a highly reliable experimental platform. The vehicle uses tuned plant dynamics with an onboard embedded attitude controller to stabilise flight. A linear SISO controller was designed to regulate flyer attitude.

Attitude observability of a loosely-coupled GPS/Visual Odometry Integrated Navigation Filter

We present a novel method for integrating GPS position estimates with position and attitude estimates derived from visual odometry using a scheme similar to a classic loosely-coupled GPS/INS integration. Under such an arrangement, we derive the error dynamics of the system and develop a Kalman Filter for estimating the errors in position and attitude. Using a control-based approach to observability, we show that the errors in both position and attitude (including yaw) are fully observable when there is a component of acceleration perpendicular to the velocity vector in the navigation frame. Numerical simulations are performed to confirm the observability analysis.

Control of aircraft for inspection of linear infrastructure

Inspection aircraft equipped with cameras and other sensors are routinely used for asset location, inspection, monitoring and hazard identification of oil-gas pipelines, roads, bridges and power transmission grids. This paper is concerned with automated flight of fixed-wing inspection aircraft to track approximately linear infrastructure. We propose a guidance law approach that seeks to maintain aircraft trajectories with desirable position and orientation properties relative to the infrastructure under inspection. Furthermore, this paper also proposes the use of an adaptive maneuver selection approach, in which maneuver primitives are adaptively selected to improve the aircraft’s attitude behaviour. We employ an integrated design methodology particularly suited for an automated inspection aircraft. Simulation studies using full nonlinear semi-coupled six degree-of-freedom equations of motion are used to illustrate the effectiveness of the proposed guidance and adaptive maneuver selection approaches in realistic flight conditions. Experimental flight test results are given to demonstrate the performance of the design.

Estimation and control for an open-source quadcopter

This paper describes modelling, estimation and control of the horizontal translational motion of an open-source and cost effective quadcopter — the MikroKopter. We determine the dynamics of its roll and pitch attitude controller, system latencies, and the units associated with the values exchanged with the vehicle over its serial port. Using this we create a horizontal-plane velocity estimator that uses data from the built-in inertial sensors and an onboard laser scanner, and implement translational control using a nested control loop architecture. We present experimental results for the model and estimator, as well as closed-loop positioning.

Integrated vision-based attitude estimation

For many years, computer vision has lured researchers with promises of a low-cost, passive, lightweight and information-rich sensor suitable for navigation purposes. The prime difficulty in vision-based navigation is that the navigation solution will continually drift with time unless external information is available, whether it be cues from the appearance of the scene, a map of features (whether built online or known a priori), or from an externally-referenced sensor. It is not merely position that is of interest in the navigation problem. Attitude (i.e. the angular orientation of a body with respect to a reference frame) is integral to a visionbased navigation solution and is often of interest in its own right (e.g. flight control). This thesis examines vision-based attitude estimation in an aerospace environment, and two methods are proposed for constraining drift in the attitude solution; one through a novel integration of optical flow and the detection of the sky horizon, and the other through a loosely-coupled integration of Visual Odometry and GPS position measurements. In the first method, roll angle, pitch angle and the three aircraft body rates are recovered though a novel method of tracking the horizon over time and integrating the horizonderived attitude information with optical flow. An image processing front-end is used to select several candidate lines in a image that may or may not correspond to the true horizon, and the optical flow is calculated for each candidate line. Using an Extended Kalman Filter (EKF), the previously estimated aircraft state is propagated using a motion model and a candidate horizon line is associated using a statistical test based on the optical flow measurements and location of the horizon in the image. Once associated, the selected horizon line, along with the associated optical flow, is used as a measurement to the EKF. To evaluate the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42° and 0.71° respectively when compared with a truth attitude source. The Cessna 172 flight resulted in pitch and roll error standard deviations of 1.79° and 1.75° respectively. In the second method for estimating attitude, a novel integrated GPS/Visual Odometry (GPS/VO) navigation filter is proposed, using a structure similar to a classic looselycoupled GPS/INS error-state navigation filter. Under such an arrangement, the error dynamics of the system are derived and a Kalman Filter is developed for estimating the errors in position and attitude. Through similar analysis to the GPS/INS problem, it is shown that the proposed filter is capable of recovering the complete attitude (i.e. pitch, roll and yaw) of the platform when subjected to acceleration not parallel to velocity for both the monocular and stereo variants of the filter. Furthermore, it is shown that under general straight line motion (e.g. constant velocity), only the component of attitude in the direction of motion is unobservable. Numerical simulations are performed to demonstrate the observability properties of the GPS/VO filter in both the monocular and stereo camera configurations. Furthermore, the proposed filter is tested on imagery collected using a Cessna 172 to demonstrate the observability properties on real-world data. The proposed GPS/VO filter does not require additional restrictions or assumptions such as platform-specific dynamics, map-matching, feature-tracking, visual loop-closing, gravity vector or additional sensors such as an IMU or magnetic compass. Since no platformspecific dynamics are required, the proposed filter is not limited to the aerospace domain and has the potential to be deployed in other platforms such as ground robots or mobile phones.